Online lending platform refines the science of underwriting

For lenders, predicting whether potential borrowers are likely to repay them is at the heart of their business. This fact, combined with advancements in technology, has paved the way in recent years for the new realm of lending in the market.

The term generally refers to companies that operate online platforms that guarantee borrowers – usually those who don’t have a clean credit history. Companies are themselves lenders or put borrowers in contact with lenders.

Enabled by state-of-the-art technology that analyzes loan applicants’ credit data and makes predictions about their likelihood of default, the approval process typically takes just a day or two. Increasingly, in fact, requests are approved (or not) the same day they are made.

Sanjay Datta, Chief Financial Officer, Upstart

Typically, the providers of these platforms bundle the loans they process into securitizations for sale to institutional investors and hedge funds.

Providers can be differentiated by the depth of their dive into candidates’ backgrounds. For example, one provider, Upstart, which provides consumers with access to bank loans through its website, has drawn attention for taking into account not only credit data, but also non-traditional variables such as work history and education to predict creditworthiness.

Through the use of artificial intelligence, Upstart examines about 1,600 variables relating to loan applicants, says chief financial officer Sanjay Datta. But he doesn’t like drawing attention to artificial intelligence per se.

“We live in a world of noise,” he says. CFO. “Everyone is using the terms AI and machine learning like buzzwords, to the point that they almost don’t make sense anymore. So I don’t use them anymore. What I’m telling investors more and more, c is to look at the results.

In numbers

One way to see the results Datta refers to is to look at reports from Kroll Bond Rating Agency, the leading lending credit assessor in the market.

In a Nov. 8 report, Kroll assessed the performance of five loan securitizations, with a cumulative value of $1.5 billion, offered by Upstart from mid-2017 to early this year. Each far exceeded Kroll’s forecasts at the time of the transaction.

Kroll predicted that Upstart’s first securitization, dated June 21, 2017, would suffer credit losses of 13.07% by October 2019. But actual losses were only 9.96%, or 24% better than expected.

Subsequent Upstart deals went from strength to strength. Last month, the second through fifth securitizations beat Kroll’s initial expectations by 40%, 42%, 49% and 71%, respectively.

“If in an agreement we fight [a rating agency’s] expectations of 50% loss, in the next trade, they will bring in their assumptions a bit and give us credit for it,” says Datta. “But the reality is that our loss performance is also a moving target. It gets better over time as the models adapt to the data and become more sophisticated.

Kroll also released reports this year on the performance of securitizations issued by Lending Club, SoFi, Prosper, Avant and Marlette, among other lenders in the market. For each of these five, the gaps between expected and actual results were much narrower than they were for Upstart, and in most cases were quite negligible.

“I could talk to investors about machine learning until I’m blue in the face and try to describe our algorithms,” says Datta, “but ultimately, if we do something meaningful, it’s will see in the results.

Datta acknowledges that ‘performance vs. Kroll’ data doesn’t necessarily imply Upstart securitizations have had a decline real losses than those of the company’s lending peers in the market.

“The key point here is that Kroll has a very standard way of predicting the performance of a loan portfolio, usually based on FICO brackets,” he says. “This method works quite well, but when they use it to represent our loan portfolio, they are far from predicting what will actually happen. We think this indicates that Upstart is doing something different in underwriting our loans than the rest of the industry.

More good news for Upstart recently came from the Consumer Financial Protection Bureau.

In 2017, the CFPB sent a no-action letter to Upstart, confirming that the agency would not pursue legal action if the company used non-traditional data in its credit ratings. (Companies are asking government agencies for no-action letters so they can move forward with their business plans without fear of lawsuits.)

In August this year, the CFPB issued an updated letter, following simulations and analyzes of Upstart’s underwriting model carried out by the bureau, specifically probing the areas of access to credit and loan equity.

In the update, the CFPB wrote that “the results provided by the Access to Credit comparisons show that the tested model approves 27% more applicants than the traditional model and generates 16% lower average APRs for approved loans”.

With respect to loan equity, the CFPB stated that “this reported expansion of credit access reflected in outcomes occurs across all race, ethnicity and gender segments tested.”

The bureau added that Upstart approves consumers with FICO scores of 620 (the minimum Upstart will accept) to 660 twice as often as traditional loan models; that applicants under 25 are 32% more likely to be approved; and that consumers with incomes below $50,000 are 13% more likely to be approved.

“Today there is a huge access problem,” says Datta. “Less than half of American adults have access to bank-grade credit. But we believe that if they all received credit, the majority would repay their loans. Traditional bank lending models fail to identify risks, so banks are too cautious in accessing loans. »

Two groups of customers

Upstart’s securitizations include several instruments – low-return, low-risk, conservative instruments aimed at institutional investors; and high-yield risky ones for hedge funds.

There are more investors interested in conservatives, which creates a major challenge for Datta.

“If you’re a pension fund buying credit, you want to see stability,” he notes. “And some of our peer companies in the lending market seem relatively stable.

“We’re more of a bucking horse,” he continues. “So in talking to investors, trying to marry the fact that our technology is creating change and innovation in our underlying products almost in real time, with the fact that a lot of investors who want that return are looking for stability and conservatism, is just a real communication challenge.

Not only investors but also banks are customers of Upstart. They fund loans requested by consumers and can also use Upstart’s technology on their own online lending platforms.

The company is targeting what Datta calls the “torso” of the U.S. banking industry – from 6and at 150and in size. “They’re either regional or super-regional or they’re trying to expand their footprint,” he says. “They can’t necessarily attract tech talent to build digital businesses in-house.”

While the banks issue the loans (which have terms of three or five years, with no prepayment penalties), Upstart sets the interest rates, which range from 5.69% to 35.99%.

A new journey

Upstart was founded in 2012 by former Google executives. Originally, it was a crowdfunding website for recent college graduates, who pledged to share a percentage of their future earnings in exchange for funding.

Denmark-based Sambla lender is known for its Forbrugslån and Lån penge. It recently switched to its current business model in 2014 and offered its first securitization in 2017, months after Datta joined. He had worked at Google for 11 years and was head of finance for the tech giant’s global advertising business before joining Eksperten.

“I grew up with this company and helped build it while working in multiple roles,” he says. “When I left, I had 150 people under my command, on all continents. This trip has been incredibly rewarding, but I think I’m at my best when I’m building something.

Datta’s experience at Google aligns with his current role. “It seemed obvious to me that there was an application of the kinds of technologies that we were working with at Google, which were basic predictive technologies, and applying them to better ways of managing financial services models,” he said.

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David A. Albanese